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Forecasting the realized volatility of Energy Stock Market: A multimodel comparison

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  • Li, Houjian
  • Zhou, Deheng
  • Hu, Jiayu
  • Li, Junwen
  • Su, Mengying
  • Guo, Lili

Abstract

The realized volatility forecasting of energy sector stocks facilitates the establishment of corresponding risk warning mechanisms and investor decisions. In this paper, we collected two different energy sector indices and used different methods, namely principal component analysis (PCA) and sparse principal component analysis (SPCA), to extract features, and combined LSTM and GRU to construct 12 different models. The results show that the SPCA-LSTM model we constructed has the best forecasting performance in the realized volatility forecasting of energy indices, and SPCA has better forecasting results than PCA in the feature extraction stage. The results of the robustness test indicate that our results are robust.

Suggested Citation

  • Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023. "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:ecofin:v:66:y:2023:i:c:s1062940823000189
    DOI: 10.1016/j.najef.2023.101895
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    Cited by:

    1. Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024. "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    2. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).

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    More about this item

    Keywords

    Deep learning; Energy stock index; Realized volatility; LSTM;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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